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OpenAI recently released its highly anticipated model with “doctoral reasoning capabilities”, which is not what many of us think of as GPT-5, but the O1 model of OpenAI.
The operation of OpenAI o1 marks an important paradigm shift in computing resource allocation, focusing more on the training and reasoning stages. This approach makes it perform well in complex inference tasks, but is also much slower than its similar models, GPT-4o and GPT-4o mini.
That is, GPT-4o and GPT-4o mini remain preferred for applications that require fast response, image processing, or function calls. However, if your project requires advanced reasoning skills and can tolerate slightly longer response times, the o1 model is the best choice.
I have the opportunity to explore o1 through OpenAI's API, and in this blog post I will provide step-by-step instructions on how to do it. I'll show you how to connect to the API, manage costs, and finally, I'll share some tips to help you minimize spending and get better responses.
If you are looking for a more entry-level article, check out this OpenAI o1 guide.
Note: At the time of writing this tutorial, the o1 model is still in the beta phase. However, the process of connecting to the API remains the same.
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